The Internet of Things (IoT) continues its evolution, causing an extraordinary growth of traffic and processing demands. Consequently, 5G players are persuaded to change their infrastructures. In this context, Fog computing emerges as a potential solution, providing nearby resources to run IoT applications. However, the Fog and the IoT raise several challenges which decelerate the adoption of the Fog paradigm. In this paper, we consider the reconfiguration problem, i.e., how to dynamically adapt the placement of IoT applications running in the Fog, depending on application needs and evolution of resource usage. We propose and evaluate a series of reconfiguration algorithms, based on both online scheduling and online learning approaches. Through an extensive set of experiments in a realistic testbed, we demonstrate that the performance strongly and mainly depends on the quality and availability of information from both Fog infrastructure and IoT applications. Finally, we show that a reactive and greedy strategy can overcome the performance of state-of-the-art online learning algorithms, as long as the strategy has access to a little extra information.